Prior work has made good progress on this task by formulating it as a reinforcement learning (RL) problem and developing useful representations [1]}, [2]}, auxiliary tasks [3]}, data augmentation techniques [4]}, and improved reward functions [4]}. Despite this progress, end-to-end RL incurs high computational cost, has poor sample efficiency, and tends to generalize poorly to new scenes [6]}, [7]}, [4]} since skills like moving without collisions, exploration, and stopping near the object are all learned from scratch purely using RL. Modular navigation methods aim to address these issues by disentangling `where to look for an object?' and `how to navigate to \((x, y)\) ?' [6]}, [10]}. These methods have emerged as strong competitors to end-to-end RL with good sample efficiency, better generalization to new scenes, and simulation to real-world transfer [11]}, [6]}. However, since `where to look?' is formulated as an RL problem with interactive reward-based learning, these methods require extensive computational resources (\(\sim \) 8 GPUs) over multiple days for training.
